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"trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "concrete_problems": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "data_eval_spec": { "dataset_name": "PIF-Bench synthetic novel feedback trajectories", "data_schema": "One row per user-turn: hidden profile, content features, explicit feedback, implicit feedback, gold preference update, gold agent-state diff, future probe.", "evaluation_protocol": "Run the round-specific updater on every feedback event, compare predicted dimensions/targets/scope with gold state diffs, then estimate future-probe success.", "metric_definitions": { "dimension_f1": "Macro F1 between 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rows; replaces the confusing no_update_precision metric." }, "splits": { "feedback_rows": 400, "neutral_no_update_rows": 66 } } }, { "round": 7, "method_name": "Round 7 positive-signal gate", "metrics": { "round": 7, "dimension_f1": 0.7165, "target_f1": 0.7317, "update_target_f1": 0.7317, "future_probe_win_rate": 0.8374, "overgeneralization_rate": 0.0, "feedback_incorporation_rate": 0.7775, "neutral_no_update_accuracy": 1.0, "update_rate": 0.6674 }, "metrics_file": "eval/self_evolve_round_07.json", "visualization": "self_evolve/round_07.html", "diagnosed_issue": "Self-evolve round 7 dominant issue: residual_future_probe_gap.", "patch_for_next_round": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "diagnosis_text": "Self-evolve round 7 dominant issue: residual_future_probe_gap.", "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.008, "headline": "future_probe_win_rate changed +0.0080 vs previous round." }, "failure_examples": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "concrete_problems": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "data_eval_spec": { "dataset_name": "PIF-Bench synthetic novel feedback trajectories", "data_schema": "One row per user-turn: hidden profile, content features, explicit feedback, implicit feedback, gold 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"future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "splits": { "feedback_rows": 400, "neutral_no_update_rows": 66 } } }, { "round": 8, "method_name": "Round 8 confidence-calibrated router", "metrics": { "round": 8, "dimension_f1": 0.7167, "target_f1": 0.7319, "update_target_f1": 0.7319, "future_probe_win_rate": 0.8454, "overgeneralization_rate": 0.0, "feedback_incorporation_rate": 0.7775, "neutral_no_update_accuracy": 1.0, "update_rate": 0.6674 }, "metrics_file": "eval/self_evolve_round_08.json", "visualization": "self_evolve/round_08.html", "diagnosed_issue": "Self-evolve round 8 dominant issue: residual_future_probe_gap.", "patch_for_next_round": "Use future-probe-aware reranking 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], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "concrete_problems": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "data_eval_spec": { "dataset_name": "PIF-Bench synthetic novel feedback trajectories", "data_schema": "One row per user-turn: hidden profile, content features, explicit feedback, implicit feedback, gold preference update, gold agent-state diff, future probe.", "evaluation_protocol": "Run the round-specific updater on every feedback event, compare predicted dimensions/targets/scope with gold state diffs, then estimate future-probe success.", "metric_definitions": { "dimension_f1": "Macro F1 between predicted preference dimensions and gold dimensions.", "target_f1": "Macro F1 between predicted agent-state targets and gold targets.", "future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "sample_count": 466, "metrics": { "dimension_f1": "Macro F1 between predicted preference dimensions and gold dimensions.", "target_f1": "Macro F1 between predicted agent-state targets and gold targets.", "future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "splits": { "feedback_rows": 400, "neutral_no_update_rows": 66 } } }, { "round": 9, "method_name": "Round 9 future-probe-aware reranker", "metrics": { "round": 9, "dimension_f1": 0.7167, "target_f1": 0.7319, "update_target_f1": 0.7319, "future_probe_win_rate": 0.858, "overgeneralization_rate": 0.0, "feedback_incorporation_rate": 0.7775, "neutral_no_update_accuracy": 1.0, "update_rate": 0.6674 }, "metrics_file": "eval/self_evolve_round_09.json", "visualization": "self_evolve/round_09.html", "diagnosed_issue": "Self-evolve round 9 dominant issue: residual_future_probe_gap.", "patch_for_next_round": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "diagnosis_text": "Self-evolve round 9 dominant issue: residual_future_probe_gap.", "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.0126, "headline": "future_probe_win_rate changed +0.0126 vs previous round." }, "failure_examples": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "concrete_problems": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "data_eval_spec": { "dataset_name": "PIF-Bench synthetic novel feedback trajectories", "data_schema": "One row per user-turn: hidden profile, content features, explicit feedback, implicit feedback, gold preference update, gold agent-state diff, future probe.", "evaluation_protocol": "Run the round-specific updater on every feedback event, compare predicted dimensions/targets/scope with gold state diffs, then estimate future-probe success.", "metric_definitions": { "dimension_f1": "Macro F1 between predicted preference dimensions and gold dimensions.", "target_f1": "Macro F1 between predicted agent-state targets and gold targets.", "future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "sample_count": 466, "metrics": { "dimension_f1": "Macro F1 between predicted preference dimensions and gold dimensions.", "target_f1": "Macro F1 between predicted agent-state targets and gold targets.", "future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "splits": { "feedback_rows": 400, "neutral_no_update_rows": 66 } } }, { "round": 10, "method_name": "Round 10 consolidated PUMA-lite v2", "metrics": { "round": 10, "dimension_f1": 0.7167, "target_f1": 0.7319, "update_target_f1": 0.7319, "future_probe_win_rate": 0.8632, "overgeneralization_rate": 0.0, "feedback_incorporation_rate": 0.7775, "neutral_no_update_accuracy": 1.0, "update_rate": 0.6674 }, "metrics_file": "eval/self_evolve_round_10.json", "visualization": "self_evolve/round_10.html", "diagnosed_issue": "Self-evolve round 10 dominant issue: residual_future_probe_gap.", "patch_for_next_round": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "diagnosis_text": "Self-evolve round 10 dominant issue: residual_future_probe_gap.", "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.0052, "headline": "future_probe_win_rate changed +0.0052 vs previous round." }, "failure_examples": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "concrete_problems": [ { "sample_id": "u_016_turn_04", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_020_turn_02", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_022_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "female_agency", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=female_agency,pacing", "missed_targets=critic.checklist,planner.policy,reranker.policy" ], "severity": 6.1, "evidence": [] }, { "sample_id": "u_022_turn_07", "comment": "不要再用误会梗了", "gold_dimensions": [ "female_agency", "lore_density", "trope_misunderstanding" ], "predicted_dimensions": [ "trope_misunderstanding" ], "gold_targets": [ "critic.checklist", "planner.policy", "reranker.policy", "retriever.policy" ], "predicted_targets": [ "planner.policy" ], "predicted_scope": "global_user", "problems": [ "missed_dimensions=female_agency,lore_density", "missed_targets=critic.checklist,reranker.policy,retriever.policy" ], "severity": 6.1, "evidence": [ "comment:taboo", "feature:misunderstanding_failure", "round8:explicit_comment_dominance_with_multicause_guard", "round8:target_completeness_pass", "round9:future_probe_rerank" ] }, { "sample_id": "u_032_turn_03", "comment": "这段不错，继续这个张力", "gold_dimensions": [ "lore_density", "pacing" ], "predicted_dimensions": [], "gold_targets": [ "critic.checklist", "planner.policy", "retriever.policy" ], "predicted_targets": [], "predicted_scope": "current_story", "problems": [ "missed_dimensions=lore_density,pacing", "missed_targets=critic.checklist,planner.policy,retriever.policy" ], "severity": 6.1, "evidence": [] } ], "data_eval_spec": { "dataset_name": "PIF-Bench synthetic novel feedback trajectories", "data_schema": "One row per user-turn: hidden profile, content features, explicit feedback, implicit feedback, gold preference update, gold agent-state diff, future probe.", "evaluation_protocol": "Run the round-specific updater on every feedback event, compare predicted dimensions/targets/scope with gold state diffs, then estimate future-probe success.", "metric_definitions": { "dimension_f1": "Macro F1 between predicted preference dimensions and gold dimensions.", "target_f1": "Macro F1 between predicted agent-state targets and gold targets.", "future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "sample_count": 466, "metrics": { "dimension_f1": "Macro F1 between predicted preference dimensions and gold dimensions.", "target_f1": "Macro F1 between predicted agent-state targets and gold targets.", "future_probe_win_rate": "Mean synthetic win probability on near/far/anti-overgeneralization future probes.", "overgeneralization_rate": "Fraction of predicted updates that incorrectly globalize local/story feedback; zero is good.", "neutral_no_update_accuracy": "Accuracy on neutral/no-gold-update rows; replaces the confusing no_update_precision metric." }, "splits": { "feedback_rows": 400, "neutral_no_update_rows": 66 } } } ] }, "long_horizon": { "iterations": 10, "metrics_jsonl": "long_horizon/metrics.jsonl", "rounds": [ { "round": 1, "method_name": "final-turn blame baseline", "metrics": { "round": 1, "culprit_step_accuracy": 0.0, "culprit_dimension_accuracy": 0.7917, "credit_mrr": 0.2863, "repair_gain": 0.076, "counterfactual_repair_gain": 0.076, "credit_calibration": 0.38 }, "metrics_file": "eval/long_horizon_round_01.json", "visualization": "long_horizon/round_01.html", "diagnosed_issue": "Long-horizon round 1 dominant issue: temporal_credit_blame_errors.", "patch_for_next_round": "Move beyond final-turn blame by scanning trajectory-wide evidence and delayed-feedback text.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "temporal_credit_blame_errors", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 3 }, "diagnosis_text": "Long-horizon round 1 dominant issue: temporal_credit_blame_errors.", "selected_next_patch": "Move beyond final-turn blame by scanning trajectory-wide evidence and delayed-feedback text." }, "selected_next_patch": "Move beyond final-turn blame by scanning trajectory-wide evidence and delayed-feedback text.", "comparison_to_previous": { "status": "baseline", "primary_metric": "culprit_step_accuracy", "primary_delta": null, "headline": "Baseline round; no previous round exists yet." }, "failure_examples": [ { "episode_id": "trace_0000", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 7, "gold_dimension": "lore_density", "predicted_dimension": "pacing", "top_candidates": [ { "step": 7, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "female_agency" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=7", "dimension_miss:gold=lore_density:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0004", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 4, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 1.0, "reason": "recency", "tags": [ "female_agency" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "lore_density" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 5, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 5, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [ "female_agency", "trope_misunderstanding" ] } ], "problems": [ "step_miss:gold=4:pred=5", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0019", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 1, "predicted_step": 7, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 7, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "trope_misunderstanding" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [] } ], "problems": [ "step_miss:gold=1:pred=7", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0022", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 1, "predicted_step": 6, "gold_dimension": "lore_density", "predicted_dimension": "pacing", "top_candidates": [ { "step": 6, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "lore_density" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [] } ], "problems": [ "step_miss:gold=1:pred=6", "dimension_miss:gold=lore_density:pred=pacing" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0000", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 7, "gold_dimension": "lore_density", "predicted_dimension": "pacing", "top_candidates": [ { "step": 7, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "female_agency" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=7", "dimension_miss:gold=lore_density:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0004", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 4, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 1.0, "reason": "recency", "tags": [ "female_agency" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "lore_density" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 5, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 5, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [ "female_agency", "trope_misunderstanding" ] } ], "problems": [ "step_miss:gold=4:pred=5", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0019", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 1, "predicted_step": 7, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 7, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "trope_misunderstanding" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [] } ], "problems": [ "step_miss:gold=1:pred=7", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0022", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 1, "predicted_step": 6, "gold_dimension": "lore_density", "predicted_dimension": "pacing", "top_candidates": [ { "step": 6, "score": 1.0, "reason": "recency", "tags": [ "pacing" ] }, { "step": 1, "score": 0.0, "reason": "recency", "tags": [ "lore_density" ] }, { "step": 2, "score": 0.0, "reason": "recency", "tags": [] } ], "problems": [ "step_miss:gold=1:pred=6", "dimension_miss:gold=lore_density:pred=pacing" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 2, "method_name": "dimension evidence scan", "metrics": { "round": 2, "culprit_step_accuracy": 0.65, "culprit_dimension_accuracy": 0.8917, "credit_mrr": 0.8167, "repair_gain": 0.1461, "counterfactual_repair_gain": 0.1461, "credit_calibration": 0.6277 }, "metrics_file": "eval/long_horizon_round_02.json", "visualization": "long_horizon/round_02.html", "diagnosed_issue": "Long-horizon round 2 dominant issue: hard_distractor_temporal_ambiguity.", "patch_for_next_round": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "hard_distractor_temporal_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 2 dominant issue: hard_distractor_temporal_ambiguity.", "selected_next_patch": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit." }, "selected_next_patch": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit.", "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.65, "headline": "culprit_step_accuracy changed +0.6500 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:trope_misunderstanding", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 1, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0043", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "pacing", "predicted_dimension": "trope_misunderstanding", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "text+tag:pacing+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding", "pacing" ] }, { "step": 3, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=pacing:pred=trope_misunderstanding" ], "severity": 2.7 }, { "episode_id": "trace_0050", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 2, "predicted_step": 1, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 1, "score": 2.35, "reason": "tag:pacing+text+tag:trope_misunderstanding", "tags": [ "pacing", "trope_misunderstanding" ] }, { "step": 2, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 3, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=2:pred=1", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:trope_misunderstanding", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 1, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "tag:female_agency+text+tag:lore_density", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:lore_density", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.55, "reason": "tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0043", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "pacing", "predicted_dimension": "trope_misunderstanding", "top_candidates": [ { "step": 2, "score": 2.35, "reason": "text+tag:pacing+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding", "pacing" ] }, { "step": 3, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] }, { "step": 4, "score": 1.8, "reason": "text+tag:pacing", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=pacing:pred=trope_misunderstanding" ], "severity": 2.7 }, { "episode_id": "trace_0050", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 2, "predicted_step": 1, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 1, "score": 2.35, "reason": "tag:pacing+text+tag:trope_misunderstanding", "tags": [ "pacing", "trope_misunderstanding" ] }, { "step": 2, "score": 1.8, "reason": "text+tag:trope_misunderstanding", "tags": [ "trope_misunderstanding" ] }, { "step": 3, "score": 0.0, "reason": "weak_evidence", "tags": [] } ], "problems": [ "step_miss:gold=2:pred=1", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 3, "method_name": "temporal utility window", "metrics": { "round": 3, "culprit_step_accuracy": 0.925, "culprit_dimension_accuracy": 0.9333, "credit_mrr": 0.9625, "repair_gain": 0.1846, "counterfactual_repair_gain": 0.1846, "credit_calibration": 0.908 }, "metrics_file": "eval/long_horizon_round_03.json", "visualization": "long_horizon/round_03.html", "diagnosed_issue": "Long-horizon round 3 dominant issue: culprit_dimension_evidence_gap.", "patch_for_next_round": "Fuse delayed-feedback dimension tokens with per-turn feature tags, severity, and repairability.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "culprit_dimension_evidence_gap", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 3 dominant issue: culprit_dimension_evidence_gap.", "selected_next_patch": "Fuse delayed-feedback dimension tokens with per-turn feature tags, severity, and repairability." }, "selected_next_patch": "Fuse delayed-feedback dimension tokens with per-turn feature tags, severity, and repairability.", "comparison_to_previous": { "status": "improved", "primary_metric": "credit_calibration", "primary_delta": 0.2803, "headline": "credit_calibration changed +0.2803 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.9202, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 2.8021, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.73, "reason": "tag:pacing+temporal_window", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.389, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 2.9512, "reason": "text+tag:lore_density+fast_swipe+temporal_window", "tags": [ "lore_density" ] }, { "step": 5, "score": 0.3, "reason": "temporal_window", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.6219, "reason": "tag:female_agency+text+tag:lore_density+temporal_window", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.1, "reason": "text+tag:lore_density+temporal_window", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0050", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 2, "predicted_step": 1, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 1, "score": 2.4786, "reason": "tag:pacing+text+tag:trope_misunderstanding+temporal_window", "tags": [ "pacing", "trope_misunderstanding" ] }, { "step": 2, "score": 2.2634, "reason": "text+tag:trope_misunderstanding+temporal_window", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.3801, "reason": "temporal_window", "tags": [] } ], "problems": [ "step_miss:gold=2:pred=1", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 2.8963, "reason": "tag:female_agency+text+tag:lore_density+temporal_window", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1, "reason": "text+tag:lore_density+temporal_window", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.0523, "reason": "tag:female_agency+temporal_window", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.9202, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 2.8021, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.73, "reason": "tag:pacing+temporal_window", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.389, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 2.9512, "reason": "text+tag:lore_density+fast_swipe+temporal_window", "tags": [ "lore_density" ] }, { "step": 5, "score": 0.3, "reason": "temporal_window", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.6219, "reason": "tag:female_agency+text+tag:lore_density+temporal_window", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.1, "reason": "text+tag:lore_density+temporal_window", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0050", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 2, "predicted_step": 1, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "pacing", "top_candidates": [ { "step": 1, "score": 2.4786, "reason": "tag:pacing+text+tag:trope_misunderstanding+temporal_window", "tags": [ "pacing", "trope_misunderstanding" ] }, { "step": 2, "score": 2.2634, "reason": "text+tag:trope_misunderstanding+temporal_window", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.3801, "reason": "temporal_window", "tags": [] } ], "problems": [ "step_miss:gold=2:pred=1", "dimension_miss:gold=trope_misunderstanding:pred=pacing" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 2.8963, "reason": "tag:female_agency+text+tag:lore_density+temporal_window", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1, "reason": "text+tag:lore_density+temporal_window", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.0523, "reason": "tag:female_agency+temporal_window", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 4, "method_name": "causal candidate scorer", "metrics": { "round": 4, "culprit_step_accuracy": 0.95, "culprit_dimension_accuracy": 0.95, "credit_mrr": 0.975, "repair_gain": 0.2016, "counterfactual_repair_gain": 0.2016, "credit_calibration": 0.932 }, "metrics_file": "eval/long_horizon_round_04.json", "visualization": "long_horizon/round_04.html", "diagnosed_issue": "Long-horizon round 4 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 4 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.025, "headline": "culprit_step_accuracy changed +0.0250 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.389, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.0958, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 5, "score": 0.3102, "reason": "temporal_window+repairability", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.6219, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.202, "reason": "text+tag:lore_density+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window+repairability", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 2.9125, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.172, "reason": "text+tag:lore_density+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.0523, "reason": "tag:female_agency+temporal_window+repairability", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.4799, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.4643, "reason": "text+tag:pacing+temporal_window+repairability", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2571, "reason": "temporal_window+repairability", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0114", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 3, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "trope_misunderstanding", "top_candidates": [ { "step": 2, "score": 2.6776, "reason": "tag:trope_misunderstanding+text+tag:lore_density+temporal_window+repairability", "tags": [ "trope_misunderstanding", "lore_density" ] }, { "step": 3, "score": 2.4527, "reason": "text+tag:lore_density+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 4, "score": 0.2915, "reason": "temporal_window+repairability", "tags": [] } ], "problems": [ "step_miss:gold=3:pred=2", "dimension_miss:gold=lore_density:pred=trope_misunderstanding" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.389, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.0958, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 5, "score": 0.3102, "reason": "temporal_window+repairability", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.6219, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.202, "reason": "text+tag:lore_density+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window+repairability", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 2.9125, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.172, "reason": "text+tag:lore_density+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.0523, "reason": "tag:female_agency+temporal_window+repairability", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.4799, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.4643, "reason": "text+tag:pacing+temporal_window+repairability", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2571, "reason": "temporal_window+repairability", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0114", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 3, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "trope_misunderstanding", "top_candidates": [ { "step": 2, "score": 2.6776, "reason": "tag:trope_misunderstanding+text+tag:lore_density+temporal_window+repairability", "tags": [ "trope_misunderstanding", "lore_density" ] }, { "step": 3, "score": 2.4527, "reason": "text+tag:lore_density+temporal_window+repairability", "tags": [ "lore_density" ] }, { "step": 4, "score": 0.2915, "reason": "temporal_window+repairability", "tags": [] } ], "problems": [ "step_miss:gold=3:pred=2", "dimension_miss:gold=lore_density:pred=trope_misunderstanding" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 5, "method_name": "trace verifier + repair planner", "metrics": { "round": 5, "culprit_step_accuracy": 0.9583, "culprit_dimension_accuracy": 0.9583, "credit_mrr": 0.9792, "repair_gain": 0.2153, "counterfactual_repair_gain": 0.2153, "credit_calibration": 0.94 }, "metrics_file": "eval/long_horizon_round_05.json", "visualization": "long_horizon/round_05.html", "diagnosed_issue": "Long-horizon round 5 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 5 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0137, "headline": "repair_gain changed +0.0137 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3148, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.1533, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8043, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.2341, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.4437, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+weak_early_distractor_penalty+calibrated_utility_gap", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.2274, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3074, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1781, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1382, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.8919, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.5421, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3148, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.1533, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8043, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.2341, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.4437, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+weak_early_distractor_penalty+calibrated_utility_gap", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.2274, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3074, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1781, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1382, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.8919, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.5421, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 6, "method_name": "Round 6 tie-aware trace logger", "metrics": { "round": 6, "culprit_step_accuracy": 0.9583, "culprit_dimension_accuracy": 0.9583, "credit_mrr": 0.9792, "repair_gain": 0.2154, "counterfactual_repair_gain": 0.2154, "credit_calibration": 0.94 }, "metrics_file": "eval/long_horizon_round_06.json", "visualization": "long_horizon/round_06.html", "diagnosed_issue": "Long-horizon round 6 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 6 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0001, "headline": "repair_gain changed +0.0001 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3478, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.1975, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8512, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.2963, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.4437, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+weak_early_distractor_penalty+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.2274, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3406, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1781, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.9365, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.5639, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3478, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.1975, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8512, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.2963, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0034", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 2.4437, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+weak_early_distractor_penalty+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 4, "score": 2.2274, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.8, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3406, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1781, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.9365, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.5639, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 7, "method_name": "Round 7 verifier fallback", "metrics": { "round": 7, "culprit_step_accuracy": 0.9667, "culprit_dimension_accuracy": 0.9667, "credit_mrr": 0.9833, "repair_gain": 0.2167, "counterfactual_repair_gain": 0.2167, "credit_calibration": 0.948 }, "metrics_file": "eval/long_horizon_round_07.json", "visualization": "long_horizon/round_07.html", "diagnosed_issue": "Long-horizon round 7 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 4, "dimension_miss": 4, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 7 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.0084, "headline": "culprit_step_accuracy changed +0.0084 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3478, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.1975, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8512, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.2963, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3406, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1781, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.9365, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.5639, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3478, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.1975, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8512, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.2963, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3406, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1781, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.9365, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.5639, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 8, "method_name": "Round 8 margin-calibrated confidence", "metrics": { "round": 8, "culprit_step_accuracy": 0.9667, "culprit_dimension_accuracy": 0.9667, "credit_mrr": 0.9833, "repair_gain": 0.2168, "counterfactual_repair_gain": 0.2168, "credit_calibration": 0.948 }, "metrics_file": "eval/long_horizon_round_08.json", "visualization": "long_horizon/round_08.html", "diagnosed_issue": "Long-horizon round 8 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 4, "dimension_miss": 4, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 8 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0001, "headline": "repair_gain changed +0.0001 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3478, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.2695, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8512, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.3397, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3455, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1997, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.9559, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.6116, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.3478, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.2695, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.8512, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.3397, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.3455, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.1997, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 3.9559, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.6116, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 9, "method_name": "Round 9 partial-credit multi-cause scorer", "metrics": { "round": 9, "culprit_step_accuracy": 0.9667, "culprit_dimension_accuracy": 0.9667, "credit_mrr": 0.9833, "repair_gain": 0.2169, "counterfactual_repair_gain": 0.2169, "credit_calibration": 0.948 }, "metrics_file": "eval/long_horizon_round_09.json", "visualization": "long_horizon/round_09.html", "diagnosed_issue": "Long-horizon round 9 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 4, "dimension_miss": 4, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 9 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0001, "headline": "repair_gain changed +0.0001 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.4678, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.3895, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.9712, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.4597, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.4655, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.3197, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 4.0759, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.7316, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0008", "delayed_comment": "那个误会梗埋得太早，后面解释让我出戏。", "gold_step": 4, "predicted_step": 2, "gold_dimension": "trope_misunderstanding", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 2, "score": 3.4678, "reason": "tag:female_agency+text+tag:trope_misunderstanding+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "trope_misunderstanding" ] }, { "step": 4, "score": 3.3895, "reason": "text+tag:trope_misunderstanding+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "trope_misunderstanding" ] }, { "step": 5, "score": 0.7454, "reason": "tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "pacing" ] } ], "problems": [ "step_miss:gold=4:pred=2", "dimension_miss:gold=trope_misunderstanding:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.9712, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.4597, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.4655, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.3197, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 4.0759, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.7316, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } }, { "round": 10, "method_name": "Round 10 CREDIT-TRACE v2", "metrics": { "round": 10, "culprit_step_accuracy": 0.975, "culprit_dimension_accuracy": 0.975, "credit_mrr": 0.9875, "repair_gain": 0.2188, "counterfactual_repair_gain": 0.2188, "credit_calibration": 0.956 }, "metrics_file": "eval/long_horizon_round_10.json", "visualization": "long_horizon/round_10.html", "diagnosed_issue": "Long-horizon round 10 dominant issue: multi_cause_trace_ambiguity.", "patch_for_next_round": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "patch_source": "autodiagnosed", "auto_diagnosis": { "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 3, "dimension_miss": 3, "low_rank_gold": 0 }, "diagnosis_text": "Long-horizon round 10 dominant issue: multi_cause_trace_ambiguity.", "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.0083, "headline": "culprit_step_accuracy changed +0.0083 vs previous round." }, "failure_examples": [ { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.9712, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.5397, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension+round10_oracle_free_stability_probe", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.4655, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.3997, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension+round10_oracle_free_stability_probe", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 4.0759, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.8116, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension+round10_oracle_free_stability_probe", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "concrete_problems": [ { "episode_id": "trace_0017", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 6, "predicted_step": 4, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 4, "score": 3.9712, "reason": "tag:female_agency+text+tag:lore_density+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 6, "score": 3.5397, "reason": "text+tag:lore_density+fast_swipe+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension+round10_oracle_free_stability_probe", "tags": [ "lore_density" ] }, { "step": 3, "score": 0.3354, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=4", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0094", "delayed_comment": "前面设定解释太密，后面节奏被拖住了。", "gold_step": 5, "predicted_step": 3, "gold_dimension": "lore_density", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 3, "score": 3.4655, "reason": "tag:female_agency+text+tag:lore_density+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "lore_density" ] }, { "step": 5, "score": 2.3997, "reason": "text+tag:lore_density+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension+round10_oracle_free_stability_probe", "tags": [ "lore_density" ] }, { "step": 7, "score": 1.1654, "reason": "tag:female_agency+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [ "female_agency" ] } ], "problems": [ "step_miss:gold=5:pred=3", "dimension_miss:gold=lore_density:pred=female_agency" ], "severity": 2.7 }, { "episode_id": "trace_0101", "delayed_comment": "读到后面才发现前面铺垫太拖，剧情推进慢了。", "gold_step": 6, "predicted_step": 5, "gold_dimension": "pacing", "predicted_dimension": "female_agency", "top_candidates": [ { "step": 5, "score": 4.0759, "reason": "text+tag:pacing+tag:female_agency+fast_swipe+temporal_window+repairability+verified_first_severe_introduction+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension", "tags": [ "female_agency", "pacing" ] }, { "step": 6, "score": 2.8116, "reason": "text+tag:pacing+temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration+round9_partial_credit_dimension+round10_oracle_free_stability_probe", "tags": [ "pacing" ] }, { "step": 4, "score": 0.2811, "reason": "temporal_window+repairability+calibrated_utility_gap+tie_aware_utility_drop+round8_margin_calibration", "tags": [] } ], "problems": [ "step_miss:gold=6:pred=5", "dimension_miss:gold=pacing:pred=female_agency" ], "severity": 2.7 } ], "data_eval_spec": { "dataset_name": "TRACE-USER-Bench synthetic delayed-feedback traces", "data_schema": "One row per multi-turn episode: turn trace, utility/implicit feedback, delayed comment, gold culprit step/dimension, counterfactual repairs.", "evaluation_protocol": "Rank culprit steps from the full trace, compare top prediction and reciprocal rank to gold, then estimate counterfactual repair gain and confidence calibration.", "metric_definitions": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "sample_count": 120, "metrics": { "culprit_step_accuracy": "Exact-match accuracy for the delayed-feedback culprit step.", "culprit_dimension_accuracy": "Accuracy for the causal preference/content dimension.", "credit_mrr": "Mean reciprocal rank of the gold culprit step.", "repair_gain": "Mean expected utility gain from the selected counterfactual repair.", "credit_calibration": "One minus confidence error; higher means confidence tracks correctness." }, "splits": { "culprit_dimensions": { "lore_density": 27, "pacing": 35, "trope_misunderstanding": 32, "female_agency": 26 } } } } ] }, "journal": "journal/research_journal.json", "journal_entries": [ { "topic": "self_evolve", "round": 1, "method_name": "explicit-only baseline", "dominant_failure_mode": "implicit_feedback_blind_spots", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 5, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "baseline", "primary_metric": "future_probe_win_rate", "primary_delta": null, "headline": "Baseline round; no previous round exists yet." }, "selected_next_patch": "Add dwell/fast-swipe/continue calibration and content-feature evidence for silent reader dissatisfaction.", "visualization": "self_evolve/round_01.html", "metrics_file": "eval/self_evolve_round_01.json" }, { "topic": "self_evolve", "round": 2, "method_name": "implicit calibrated updater", "dominant_failure_mode": "scope_overgeneralization", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 5, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.0119, "headline": "future_probe_win_rate changed +0.0119 vs previous round." }, "selected_next_patch": "Add verifier to distinguish current-story/current-arc updates from durable global user preferences.", "visualization": "self_evolve/round_02.html", "metrics_file": "eval/self_evolve_round_02.json" }, { "topic": "self_evolve", "round": 3, "method_name": "state-diff target router", "dominant_failure_mode": "scope_overgeneralization", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 5, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "target_f1", "primary_delta": 0.3679, "headline": "target_f1 changed +0.3679 vs previous round." }, "selected_next_patch": "Add verifier to distinguish current-story/current-arc updates from durable global user preferences.", "visualization": "self_evolve/round_03.html", "metrics_file": "eval/self_evolve_round_03.json" }, { "topic": "self_evolve", "round": 4, "method_name": "scope verifier", "dominant_failure_mode": "framework_target_gap_after_scope_fix", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "overgeneralization_rate", "primary_delta": -0.8553, "headline": "overgeneralization_rate changed -0.8553 vs previous round." }, "selected_next_patch": "Run a verifier pass that re-routes each surviving dimension to all primary framework targets.", "visualization": "self_evolve/round_04.html", "metrics_file": "eval/self_evolve_round_04.json" }, { "topic": "self_evolve", "round": 5, "method_name": "self-debugged PUMA-lite", "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "target_f1", "primary_delta": 0.1106, "headline": "target_f1 changed +0.1106 vs previous round." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "visualization": "self_evolve/round_05.html", "metrics_file": "eval/self_evolve_round_05.json" }, { "topic": "self_evolve", "round": 6, "method_name": "Round 6 neutral-control evaluator", "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.0083, "headline": "future_probe_win_rate changed +0.0083 vs previous round." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "visualization": "self_evolve/round_06.html", "metrics_file": "eval/self_evolve_round_06.json" }, { "topic": "self_evolve", "round": 7, "method_name": "Round 7 positive-signal gate", "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.008, "headline": "future_probe_win_rate changed +0.0080 vs previous round." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "visualization": "self_evolve/round_07.html", "metrics_file": "eval/self_evolve_round_07.json" }, { "topic": "self_evolve", "round": 8, "method_name": "Round 8 confidence-calibrated router", "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.008, "headline": "future_probe_win_rate changed +0.0080 vs previous round." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "visualization": "self_evolve/round_08.html", "metrics_file": "eval/self_evolve_round_08.json" }, { "topic": "self_evolve", "round": 9, "method_name": "Round 9 future-probe-aware reranker", "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.0126, "headline": "future_probe_win_rate changed +0.0126 vs previous round." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "visualization": "self_evolve/round_09.html", "metrics_file": "eval/self_evolve_round_09.json" }, { "topic": "self_evolve", "round": 10, "method_name": "Round 10 consolidated PUMA-lite v2", "dominant_failure_mode": "residual_future_probe_gap", "evidence": { "missed_dimensions": 5, "missed_targets": 5, "overgeneralized_scope": 0, "extra_dimensions": 0, "neutral_errors": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "future_probe_win_rate", "primary_delta": 0.0052, "headline": "future_probe_win_rate changed +0.0052 vs previous round." }, "selected_next_patch": "Use future-probe-aware reranking and hard anti-overgeneralization probes; keep AHEAD disabled.", "visualization": "self_evolve/round_10.html", "metrics_file": "eval/self_evolve_round_10.json" }, { "topic": "long_horizon", "round": 1, "method_name": "final-turn blame baseline", "dominant_failure_mode": "temporal_credit_blame_errors", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 3 }, "comparison_to_previous": { "status": "baseline", "primary_metric": "culprit_step_accuracy", "primary_delta": null, "headline": "Baseline round; no previous round exists yet." }, "selected_next_patch": "Move beyond final-turn blame by scanning trajectory-wide evidence and delayed-feedback text.", "visualization": "long_horizon/round_01.html", "metrics_file": "eval/long_horizon_round_01.json" }, { "topic": "long_horizon", "round": 2, "method_name": "dimension evidence scan", "dominant_failure_mode": "hard_distractor_temporal_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.65, "headline": "culprit_step_accuracy changed +0.6500 vs previous round." }, "selected_next_patch": "Add temporal utility-drop windows so early weak distractors do not steal credit from the real culprit.", "visualization": "long_horizon/round_02.html", "metrics_file": "eval/long_horizon_round_02.json" }, { "topic": "long_horizon", "round": 3, "method_name": "temporal utility window", "dominant_failure_mode": "culprit_dimension_evidence_gap", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "credit_calibration", "primary_delta": 0.2803, "headline": "credit_calibration changed +0.2803 vs previous round." }, "selected_next_patch": "Fuse delayed-feedback dimension tokens with per-turn feature tags, severity, and repairability.", "visualization": "long_horizon/round_03.html", "metrics_file": "eval/long_horizon_round_03.json" }, { "topic": "long_horizon", "round": 4, "method_name": "causal candidate scorer", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.025, "headline": "culprit_step_accuracy changed +0.0250 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_04.html", "metrics_file": "eval/long_horizon_round_04.json" }, { "topic": "long_horizon", "round": 5, "method_name": "trace verifier + repair planner", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0137, "headline": "repair_gain changed +0.0137 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_05.html", "metrics_file": "eval/long_horizon_round_05.json" }, { "topic": "long_horizon", "round": 6, "method_name": "Round 6 tie-aware trace logger", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 5, "dimension_miss": 5, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0001, "headline": "repair_gain changed +0.0001 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_06.html", "metrics_file": "eval/long_horizon_round_06.json" }, { "topic": "long_horizon", "round": 7, "method_name": "Round 7 verifier fallback", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 4, "dimension_miss": 4, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.0084, "headline": "culprit_step_accuracy changed +0.0084 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_07.html", "metrics_file": "eval/long_horizon_round_07.json" }, { "topic": "long_horizon", "round": 8, "method_name": "Round 8 margin-calibrated confidence", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 4, "dimension_miss": 4, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0001, "headline": "repair_gain changed +0.0001 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_08.html", "metrics_file": "eval/long_horizon_round_08.json" }, { "topic": "long_horizon", "round": 9, "method_name": "Round 9 partial-credit multi-cause scorer", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 4, "dimension_miss": 4, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "repair_gain", "primary_delta": 0.0001, "headline": "repair_gain changed +0.0001 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_09.html", "metrics_file": "eval/long_horizon_round_09.json" }, { "topic": "long_horizon", "round": 10, "method_name": "Round 10 CREDIT-TRACE v2", "dominant_failure_mode": "multi_cause_trace_ambiguity", "evidence": { "step_miss": 3, "dimension_miss": 3, "low_rank_gold": 0 }, "comparison_to_previous": { "status": "improved", "primary_metric": "culprit_step_accuracy", "primary_delta": 0.0083, "headline": "culprit_step_accuracy changed +0.0083 vs previous round." }, "selected_next_patch": "Keep CREDIT-TRACE verifier; next expansion should add multi-cause labels from real reader logs, not AHEAD.", "visualization": "long_horizon/round_10.html", "metrics_file": "eval/long_horizon_round_10.json" } ], "dashboard": "dashboard/index.html" }